Method and apparatus for diversity combining and co-channel interference suppression

ABSTRACT

A method and apparatus for controlling an antenna array for wireless communication are described. The method uses the statistical characteristics of the received signal noise in controlling the antenna array in order to achieve directional reception in a wireless communication system and suppress co-channel interference. Furthermore, the method can be used to power space-division multiple access and can be used in conjunction with multi-carrier modulation signaling.

BACKGROUND OF THE INVENTION

(1) Field of the Invention

This invention relates to wireless communications—in particular todiversity combining in a receiver employing an antenna array in order toachieve adaptive directional reception, exploit multi-path signalreception, suppress co-channel interference and allow space divisionmultiplexing.

(2) Brief Description of Related Art

In the area of burst wireless communications the directional signaltransmission and reception enhance all the performance metrics of thecommunication links such as range, throughput rate, emitted signalpower, power dissipation, as well as link reliability and interferenceimmunity. Directionality is achieved by employing an antenna arraycontrolled by a beamformer logic at the transmitter site and a signalcombiner logic at the receiver site. Antenna arrays can also be coupledwith logic for supporting multiple communication links with spatiallyseparated users that share the same spectrum and time frame. Forexample, spatial division multiple access (SDMA) systems are based onthis notion. The above pieces of logic can be modeled in many differentways [1].

However, incorporating high performance adaptation techniques inpractical applications is a highly non-trivial task because of thecomputational complexity factor.

A number of different methods for diversity combining and co-channelinterference suppression for wireless burst communications systems havebeen proposed. However, these methods suffer from one or more weaknessessuch as the need of unrealistic modeling assumptions, high computationalcomplexity, slow convergence and the need of coupling with ad-hocalgorithms that alleviate the above.

For example, in [2] the noise power and instant subcarrier energyestimations are used for computing a diversity combiner weight vector inOFDM signaling. In particular, with reference to FIG. 1, a flowchart ofoperation of this prior art diversity combiner 10 begins with power upblock 11. When a symbol 12 is received, the noise power is estimated inblock 13 based on some unloaded carriers. For every loaded subcarrier inthe said symbol a sequence of five tasks 14 takes place. The subcarrieris received in block 15, its instant energy gets estimated in block 16and subsequently accumulated to update the subcarrier energy estimationin block 17. Next, the diversity combiner weight vector respective tothe said subcarrier gets computed in block 18 on the basis of thesubcarrier instant energy, the subcarrier energy estimation and the saidnoise power. Finally the weight vector is applied in block 19 to thediversity combiner and the data logic level is extracted. Theperformance of this method depends on the quality of the noise powerestimator and consequently the number and structure of the unloadedcarriers. In addition, the weights are estimated on the basis of energymeasurements only, so the expected quality of performance is poor andtherefore an ad-hoc co-phasing algorithm needs to be coupled within themethod. Furthermore, this method cannot be used for interferencecancellation.

In [3] and [4], two categories of algorithms for diversity combining andco-channel interference suppression are reviewed. In the first category,the direction of arrival (DOA) of the beam needs to be identified at thereceiver. This presents many deficiencies. First, DOA estimation is anextremely computation intensive process that cannot be implementedefficiently in the current art of semiconductor technology, thus itcannot find applications in high volume consumer products. Second, theDOA estimation methods are very sensitive to model imperfections such asantenna element intervals and antenna array geometry. Third, the numberof antenna elements in the antenna array limits the number of multipathsand interferers DOA based methods can cope with.

In the second category, a training sequence is required along with anestimation of the correlation with this training sequence and the inputsignal correlations. Although the problems of the algorithms in theprevious paragraph are avoided, the need for estimating the correlationsof the input signals introduces a slow convergence rate algorithm,especially in relation to multicarrier wireless communication systems.For instance, averaging over a particular subcarrier requires multiplemulticarrier symbols.

SUMMARY OF THE INVENTION

An object of this invention is to use the statistical characteristics ofthe received signal noise in controlling an antenna array in order toachieve directional reception in a wireless communication system.Another object of this invention is to suppress co-channel interference.Still another object of this invention is to power a space-divisionmultiple access wireless communication system.

Here, a method for controlling an antenna array appropriate for burstwireless communications is described. This method exhibits smart antennacharacteristics for the receiver including co-channel interferencesuppression and multi-user support. Also it can be applied in burstwireless systems employing the Orthogonal Frequency DivisionMultiplexing (OFDM) signaling scheme.

Advantages:

-   -   1. Enables non-line of sight communication.    -   2. Improves the reliability and performance of the wireless        communication system in the presence of interference.    -   3. Exploits spatial diversity in order to support multiple users        at the same frequency spectrum and time frame, thus it increases        dramatically the communication capacity.    -   4. Low computational complexity allows the use of this method in        devices targeting the consumer market.    -   5. Fast convergence.    -   6. No assumption of the statistical characteristics of the        signal or the channel is necessary.    -   7. No assumption about the antenna array geometry is necessary,        while the method is immune to antenna element placement and        element interval inaccuracies.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows the flowchart of operation of prior art diversity combining

FIG. 2 shows the flowchart of operation for diversity combining andco-channel interference suppression according to the invention

FIG. 3 shows a multi-antenna receiver according to the invention

DETAILED DESCRIPTION OF THE INVENTION

With reference to FIG. 2, a flowchart of operation of a diversitycombiner 20 in accordance with a preferred embodiment of the presentinvention is used by a receiver coupled to receive a plurality of Kinput signals using an antenna array. Each signal consists of a usefulinformation signal and an additive unwanted signal. The unwanted signalcontains additive noise, usually referred to as Additive White GaussianNoise (AWGN) in wireless communications, and may additionally containone or more interferer signals possibly of the same structure as theuseful information signal. Each input signal is characterized by a frameincluding a sequence of data blocks, said frame comprising a knowntraining data block sequence and an information data block sequence,each of said training and information sequences including at least onedata block, each data block including N samples. The flowchart beginswith power up in block 21. When a new frame is received in block 22, thechannel vectors H_(n), n=1,2, . . . ,N are estimated in block 23 basedon the received training sequence, where each channel vector has size1×K. Then, the correlation matrix R of the said unwanted signal isinitialised in block 24 to the identity matrix 1_(K) of size K×K. Foreach block in the frame 25 the diversity combining weight vectors W_(n),n=1,2, . . . ,N each one of size K×1 are computed in block 26 based onthe channel estimate and the said correlation matrix R of the unwantedsignal. Each vector W_(n) is the solution to the following minimizationproblem:Minimize W_(n)*^(T)RW_(n)  (1)Subject to H_(n)W_(n)=1  (2)that is:W _(n)=(H _(n) R ⁻¹ H _(n)*^(T))⁻¹ R ⁻¹ H _(n)*^(T) , n=1, . . . ,N  (3)where “T”, “*” and “−1” denote matrix transposition, conjugation andinversion respectively.

Then, for each sample n=1,2, . . . ,N in the block 27 a sequence of twotasks takes place. First, an input sample vector E_(n) of size 1×K isreceived in block 28. Second, the weight vector W_(n) is applied to theinput sample vector E_(n) and the logic level is extracted in block 29:{circumflex over (d)}_(n)=quantize

E_(n)W_(n)

.  (4)

The said unwanted signal is defined by:U _(n)=(E _(n) −{circumflex over (d)}H _(n))*^(T).  (5)

Finally, the said correlation matrix R of the unwanted signal iscomputed 210 by: $\begin{matrix}{R = {\sum\limits_{n = 1}^{N}\quad{U_{n}{U_{n}^{*T}.}}}} & (6)\end{matrix}$

The flowchart described above is appropriate both for directionalreception and co-channel interference (CCI) suppression.

The computation of the inverse R⁻¹ of the correlation matrix R can beobtained by direct matrix inversion or with an iterative method, forexample by using the matrix inversion lemma: $\begin{matrix}\begin{matrix}{G_{0} = {\frac{1}{\delta}I_{K}}} \\{{G_{n} = {G_{n - 1} - {G_{n - 1}{U_{n}( {1 + {U_{n}^{*T}G_{n - 1}U_{n}}} )}^{- 1}U_{n}^{*T}G_{n - 1}}}},{n = 1},\ldots\quad,N} \\{R^{- 1} = G_{N}}\end{matrix} & (7)\end{matrix}$where δ is a small positive number.

Another preferred embodiment of the present invention comprises anantenna array of L antenna elements grouped in M sub-arrays of Kelements each one. Each antenna sub-array of the plurality of Msub-arrays is coupled to feed a diversity combiner 20. The m^(th)diversity combiner 20, where m ranges in 1, 2, . . . ,M, at the n^(th)time instant produces a soft logic level {circumflex over (d)}_(n,m)following for example the steps described with reference to FIG. 2. Anoutput logic level {circumflex over (d)}_(n) is produced on the basis ofthe soft logic levels {circumflex over (d)}_(n,m), m=1, 2, . . . ,M.This can be obtained by means of averaging of the soft logic levels, orselecting one soft logic level by using some criterion, or by means ofmaximal ratio combining with respect to channel measurements.

With reference to FIG. 3, a multi-antenna receiver 30 in accordance witha preferred embodiment of the present invention comprises an OFDMdiversity combiner. An antenna array of K antenna elements 31-1 to 31-Kgenerates K samples for each subcarrier in an OFDM symbol of size N. Theantenna elements provide input to K fast Fourier transform (FFT)processors 32-1 to 32-K Each of the FFT processors produces N frequencysamples E_(nk), n=1, 2, . . . ,N for each OFDM symbol. The producedfrequency domain content is fed to a Channel Estimation Unit 33 thatcomputes the channel response H_(nk) for each antenna element k and eachsubcarrier n on the basis of a sequence of training symbols:$\begin{matrix}{{H_{nk} = {\frac{1}{c_{n}}E_{nk}}},{k = 1},\ldots\quad,K,{n = 1},\ldots\quad,N} & (8)\end{matrix}$where c_(n), n=1, 2, . . . ,N denote the noise free, real valuedtransmitted carriers of a training symbol. If the training sequenceemploys more than one training symbols, the channel response can becomputed by means of averaging. The produced estimates are fed to NWeight Extractor units 34-1 to 34-N. The n^(th) Weight Extractor unit,receives also as input the unwanted signal correlation matrix R andproduces the weight vector W_(n) according to equation (3). The producedoutputs are fed to N Decision Making units 35-1 to 35-N. The n^(th)Decision Making unit receives also as input the n^(th) output from eachFFT and it produces a logic level according to equation (4), whereE_(n)=[E_(n1)E_(n2) . . . E_(nK)], n=1, . . . ,N.

The logic levels are fed to N Unwanted Signal Generator units 36-1 to36-N. The n^(th) Unwanted Signal Generator unit receives also as inputthe n^(th) output from each FFT and it produces a column vector oflength K according to equation (5), whereH_(n)=[H_(n1)H_(n2) . . . H_(nK)], n=1, . . . ,N.

The outputs of the Unwanted Signal Generator units are fed to theUnwanted Signal Correlation Matrix Builder 37 that performs equation(6), produces the inverse of matrix R and feeds it back to the WeightExtractor units 34-1 to 34-N. Note that in (6) matrix R is computed onthe basis of the sub-carriers of one OFDM symbol. However, R can also becomputed using the sub-carriers of two or more consecutive symbols.

The output of the diversity combiner 30 comprises the logic levelsproduced by the Decision Making units 35-1 to 35-N, as well as theweight vectors produced by the Weight Extractor units 34-1 to 34-N. In aTime Division Duplex (TDD) system these weight vectors can be used bythe transmitter for the purpose of beamforming.

In another preferred embodiment of this invention, the channel estimatesare updated in the Channel Estimation Unit 33 on the basis of thereceived data (4). An exemplary update algorithm is given below:$\begin{matrix}{{H_{n}^{new} = {{( {1 - \alpha} )H_{n}^{old}} + {\alpha\frac{1}{{\hat{d}}_{n}}E_{n}}}},{n = 1},\ldots\quad,N} & (9)\end{matrix}$where α is a number in the range (0,1).

In another preferred embodiment of the multi-antenna receiver 30according to the present invention the weight vectors W_(n), n=1, 2, . .. ,N are computed byW_(n)=B_(n)Z, n=1, . . . ,N  (10)where B_(n), n=1, 2, . . . ,N are diagonal matrices of size K×K$\begin{matrix}{{B_{n} = \begin{bmatrix}{H_{n1}^{*}/{H_{n1}}^{2}} & \quad & 0 \\\quad & ⋰ & \quad \\0 & \quad & {H_{nK}^{*}/{H_{nK}}^{2}}\end{bmatrix}},{n = 1},\ldots\quad,N,} & (11)\end{matrix}$and the length K column vector Z is the solution to the followingminimization problemMinimize Z*^(T)PZ  (12)Subject to 1_(K)Z=1  (13)where 1_(K) is a row vector of size K with unit elements, P is a matrixof size K×K $\begin{matrix}{{P = {\sum\limits_{n = 1}^{N}\quad{V_{n}V_{n}^{*T}}}},} & (14) \\{V_{n},{n = 1},\ldots\quad,{N\quad{is}}} & \quad \\{{V_{n} = {B_{n}^{*}U_{n}}},{n = 1},\ldots\quad,N} & (15)\end{matrix}$and U_(n) is determined as in equation (5).

Note that the minimization problem (12)-(13) can be solved by using aleast squares algorithm such as the RLS algorithm.

In another preferred embodiment of the multi-antenna receiver 30, theweight vectors are computed on the basis of (10) where vector Z is thesolution to the minimization problem (12)-(13) and at the same time itsatisfies an inequality constraint (16)Z*^(T)Z≦γ  (16)for some positive constant γ. In this way, the dynamic range of theweight vector is better controlled.

The receiver 30 takes a fully parallel design approach. Alternatively,eg units 34-1 to 34-N can be replaced by a single unit that is able toproduce an output at a higher rate, eg N times faster. Suchserialization approach can be followed for implementing any unitappearing in multiple copies in FIG. 3. Furthermore, the describedmethod can be applied coupled with any discrete data transformer inplace of the FFT, such as the Discrete Sine Transform, Discrete CosineTransform, Discrete Wavelet Transform, etc.

The multi-antenna receiver 30 can be adapted for use in a single carriercommunication system according to the present invention by avoiding thequantization function in the Decision Making Units 35-1 to 35-N andfeeding the produced N soft logic levels to an inverse fast Fouriertransformer. The output N time-domain soft logic levels are thenquantized to produce the desired hard logic levels.

Another preferred embodiment of the diversity combiner according to thepresent invention concerns a receiver serving a number of J simultaneouscommunication links sharing the same frequency spectrum or overlappingfrequency spectrums. The objective of this embodiment is to supportmultiple communication links with spatially separated users that shareessentially the same spectrum and time frame. For example, spatialdivision multiple access (SDMA) systems are based on this notion. Thereceiver is equipped with an antenna array of K elements and employssymbols of N subcarriers.

The method described below assumes that the receiver knows the channelcharacteristics of the J different communication links. This informationfor example can be extracted by means of training sequences respectiveto the different links that are transmitted at distinct time intervals.In particular, if the receiver is used in a multi-carrier signalcommunications system, the frequency responses associated to the Jcommunication links can be computed as follows: $\begin{matrix}{{H_{n}^{(j)} = \lbrack {H_{n1}^{(j)}\quad H_{n2}^{(j)}\quad\ldots\quad H_{nK}^{(j)}} \rbrack},{n = 1},\ldots\quad,N,{j = 1},\ldots\quad,J} \\{where} \\{{H_{nk}^{(j)} = {\frac{1}{c_{n}^{(j)}}E_{nk}^{(j)}}},{k = 1},\ldots\quad,K,{n = 1},\ldots\quad,N,{j = 1},\ldots\quad,J}\end{matrix}$and C_(n) ^((j)), n=1, . . . ,N represents the j^(th) communication linktraining sequence and E_(nk) ^((j)), n=1, . . . ,N represents thecorresponding frequency domain sample received at the k^(th) antenna.

The data symbols are transmitted simultaneously implying that a receivedfrequency domain data sample E_(n) carries information from J differentsources, where E_(n) is a row vector of length K as described withreference to FIG. 3. The distinct weight vectors W_(n) ^((j)) respectiveto the j^(th) communication link, j=1, . . . ,J, can be stacked togetherin a matrix W_(n) of size K×J:W_(n)=[W_(n) ⁽¹⁾W_(n) ⁽²⁾ . . . W_(n) ^((J))], n=1, . . . ,N  (17)Similarly, let H_(n) denote the J×K matrixH_(n)=[H_(n) ^((1)T)H_(n) ^((2)T) . . . H _(n) ^((J)T)]^(T), n=1, . . .,N  (18)and d_(n) denote the vector of size 1×J of the multiple logic levelsrespective to the multiple communication links:d_(n)=[{circumflex over (d)}_(n) ⁽¹⁾{circumflex over (d)}_(n) ⁽²⁾ . . .{circumflex over (d)}_(n) ^((J))], n=1, . . . ,NAlso, let the unwanted signal U_(n) beU_(n)=(E_(n)−d_(n)H_(n))*^(T), n=1, . . . ,Nand the correlation matrix R defined as in equation (6). Consider thefollowing minimization problem:Minimize W_(n)*^(T)RW_(n)  (19)Subject to H_(n)W_(n)=I_(J)  (20)The solution in this problem is:W _(n) =R ⁻¹ H _(n)*^(T)(H _(n) R ⁻¹ H _(n)*^(T))⁻¹ ,n=1, . . . ,N  (21)For each input sample vector E_(n), n=1,2, . . . ,N the multiple logiclevels are jointly detected as follows:d_(n)=quantize

E_(n)W_(n)

  (22)

It deserves noting that in order to avoid the non-singularity of thesize J×J matrix (H_(n)R⁻¹ H_(n)*^(T)), the number K of antenna elementsshould be greater or equal to the number J of communication links.Furthermore, the noise correlation matrix R is common for the evaluationof all J weight vectors (17). Consequently, only one matrix inversion isrequired for all J communication links. Furthermore, this inversion canbe calculated recursively in N steps by using for example the matrixinversion lemma.

In another preferred embodiment of the present invention concerning adiversity combiner that serves a number of J simultaneous communicationlinks sharing the same frequency spectrum, the distinct weight vectorsW_(n) ^((j)),j=1, . . . ,J, are computed independently on the basis ofW _(n) ^((j))=(H _(n) ^((j)) R ⁻¹ H _(n) ^((j))*^(T))⁻¹ R ⁻¹ H _(n)^((j))*^(T) , n=1, . . . ,N, j=1, . . . ,J  (23)where R is a correlation matrix defined as in (6) with unwanted signalU_(n) defined as: $\begin{matrix}{U_{n} = ( {E_{n} - {\sum\limits_{j = 1}^{J}\quad{H_{n}^{(j)}{\hat{d}}_{n}^{(j)}}}} )^{*T}} & (24)\end{matrix}$and logic levels:{circumflex over (d)}_(n) ^((j))=quantize

d_(n) ^((j))

, j=1, . . . ,J  (25)The quantities d_(n) ^((j)), j=1, . . . ,J are the solution of thelinear system of J equations with J unknowns $\begin{matrix}{{d_{n}^{(j)} = {( {E_{n} - {\sum\limits_{\underset{i \neq j}{i}}\quad{H_{n}^{(i)}d_{n}^{(j)}}}} )^{*}W_{n}^{(j)}}},{j = 1},\ldots\quad,{J.}} & (26)\end{matrix}$Solving (26), substituting in (25) and stacking together the logiclevels respective to the multiple communication links we get:d _(n)=quantize

E _(n) W _(n)(H _(n) W _(n))⁻¹

  (27)where W_(n) is defined in formula (17) and its columns are computed onthe basis of (23).

In another preferred embodiment of the present invention a diversitycombiner serves multiple communication links that are categorizedaccording to their received signal strength, while the logic levelsassociated to the communication links of each category are computed inparallel and the processing respective to different categories takesplace in a serial manner. The categorization can take place on the basisof a training signal. For example, for two categories of J₁ highstrength signals and J₂ low strength signals, where J₁≧1 and J₂≧1, thelogic levels {circumflex over (d)}_(n) ^((j)), j=1, . . . ,J₁ aredetermined by using (22) or (27) and taking into account only these J₁high strength signal links while the J₂ low strength signals contributein the unwanted signal. Subsequently, the logic levels {circumflex over(d)}_(n) ^((j)), j=J₁+1, . . . ,J₁+J₂ are determined using (22) or (27)where the input sample vector E_(n) is substituted by the modifiedsample vector$E_{n}^{2} = {E_{n} - {\sum\limits_{j = 1}^{J_{1}}\quad{{\hat{d}}_{n}^{(j)}H_{n}^{(j)}}}}$and only the J₂ low strength signal links are taken into account. Theobjective of this embodiment is to reduce the computational complexityof solving equation (27) or address cases where the strengths of thereceived signals exhibit large differences. This embodiment of theinvented method can be activated in real time after the received signalstrength measurements become available.

In the above descriptions of the preferred embodiments of diversitycombiners each one of the weight vectors for one communication link isdetermined on the basis of one associated input sample vector. However,the method of the present invention can also be adapted for determiningone weight vector on the basis of two or more consecutive input samplevectors. Also, the method of the present invention can be used fordetermining two or more weight vectors associated to consecutive inputsample vectors on the basis of these consecutive input sample vectors.In particular, for the case of multi-carrier communication systems,blocks of consecutive sub-carriers in consecutive symbols can be groupedtogether for computing the respective combining weight vectors. Theseadaptations of the invented method serve two purposes. First, to exploitthe statistical dependences among input sample vectors for the benefitof interference cancellation. Second, to control the computationalcomplexity of the method.

REFERENCES

-   [1]S. Haykin, Adaptive filter theory, Prentice Hall, Englewood    Cliffs, 2^(nd) Ed., 1991.-   [2]U.S. Pat. No. 5,550,872, J. C. Liberti, D. I. Averst, T. R.    Branch and S. R. Carsello, “Method and Apparatus for Fast Fourier    Transform based Maximal Ratio Combining”, August 1996.-   [3] J. Razavilar, F. Rashid-Farrokhi and K. J. R. Liu, “Software    Radio Architecture with Smart Antennas: A Tutorial on Algorithms and    Complexity”, IEEE Trans. on Selected Areas in Communications, Vol    17, No 4, pp.662-676, April 1999.-   [4] S. Kapoor, D. J. Marchok and Y-F. Huang, “Adaptive Interference    Suppression in Multiuser Wireless OFDM Systems Using Antenna    Arrays”, IEEE Trans. on Signal Processing, Vol 47, No 12,    pp.3381-3391, December 1999.

1. A method for diversity combining and co-channel interferencesuppression in a receiver device receiving a plurality of input signalsusing an antenna array, each input signal being characterized by a framecomprising a sequence of information data blocks, each data blockincluding a number of samples, said signals being produced by at leastone transmitter device, and all transmitter devices transmitting signalssimultaneously and sharing the same frequency spectrum, comprising thesteps of: computing at least one diversity combining weight vector foreach information data block and at least one transmitter devices basedon correlation estimates of an unwanted signal and channel responsesrespective to the antenna array and at least one transmitter devices;determining input logic levels based on the input signals and thediversity combining weight vectors; and for each information data block,determining the correlations of an unwanted signal based on the inputsamples, the input logic levels and the channel responses respective tothe antenna array and at least one transmitter devices.
 2. The method ofclaim 1, wherein the data block is a multicarrier modulated symbol. 3.The method of claim 1, wherein the data block is an orthogonal frequencydivision multiplexing (OFDM) symbol.
 4. The method of claim 1, whereinthe correlations of the unwanted signal are initialized to memory storedvalues.
 5. The method of claim 4, wherein auto-correlations are assignedthe unit value and cross-correlations are assigned zero value.
 6. Themethod of claim 1, wherein the step of computing the diversity combiningweight vectors uses a recursive least squares algorithm.
 7. The methodof claim 1, wherein the said input signal frame comprises at least onesequence of training data blocks respective to the at least onetransmitter devices, each sequence of training data blocks carryinginformation from only one transmitter device, wherein the at least onesequence of training data blocks are used for estimating the channelresponses respective to the at least one transmitter devices.
 8. Themethod of claim 7, further comprising the step of updating the channelresponse estimates based on the determined logic levels of theinformation data blocks.
 9. Apparatus for diversity combining andco-channel interference suppression in a receiver device receiving aplurality of input signals using an antenna array, each signal beingcharacterized by a frame comprising a sequence of information datablocks, each data block including a number of samples, said signalsbeing produced by at least one transmitter device, and all transmitterdevices transmitting signals simultaneously and sharing the samefrequency spectrum, comprising: means for computing at least onediversity combining weight vector for each information data block and atleast one transmitter devices based on the correlation estimates of anunwanted signal and the channel responses respective to the antennaarray and at least one transmitter devices; means for determining inputlogic levels based on the input signals and the diversity combiningweight vectors; means for determining the correlations of an unwantedsignal based on the input samples, the input logic levels and thechannel responses respective to the antenna array and at least onetransmitter devices, and means producing an output on a block by blockbasis.
 10. The apparatus of claim 9, wherein the data block is amulticarrier modulated symbol.
 11. The apparatus of claim 9, wherein thedata block is an orthogonal frequency division multiplexing (OFDM)symbol.
 12. The apparatus of claim 9, wherein the correlations of theunwanted signal are initialized to memory stored values.
 13. Theapparatus of claim 9, wherein auto-correlations are assigned the unitvalue and cross-correlations are assigned zero value.
 14. The apparatusof claim 9, wherein the step of computing the diversity combining weightvectors uses a recursive least squares algorithm.
 15. The apparatus ofclaim 9, wherein said input signal frame comprises at least one sequenceof training data blocks respective to at least one transmitter devices,each sequence of training data blocks carrying information from only onetransmitter device, wherein at least one sequence of training datablocks are used for estimating the channel responses respective to atleast one transmitter devices.
 16. The apparatus of claim 15, whereinsaid apparatus further comprises the means of updating the channelresponse estimates based on the determined logic levels of theinformation data blocks.